Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Denoising through wavelet shrinkage: an empirical study

Denoising through wavelet shrinkage: an empirical study Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The idea is to transform the data into the wavelet basis, where the “large” coefficients are mainly the signal, and the “smaller” ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. We evaluate several 2-D denoising procedures using test images corrupted with additive Gaussian noise. We consider global, level-dependent, and subband-dependent implementations of these techniques. Our results, using the mean squared error as a measure of the quality of denoising, show that the SureShrink and the BayesShrink methods consistently outperform the other wavelet-based techniques. In contrast, we found that a combination of simple spatial filters lead to images that were grainier with smoother edges, though the error was smaller than in the wavelet-based methods. © 2003 SPIE and IS&T. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Electronic Imaging SPIE

Denoising through wavelet shrinkage: an empirical study

Fodor, Imola K.
Journal of Electronic Imaging , Volume 12 (1) – Jan 1, 2003
Download PDF
10 pages

Loading next page...
 
/lp/spie/denoising-through-wavelet-shrinkage-an-empirical-study-YRlgMzDVs8

References (34)

Publisher
SPIE
Copyright
Copyright © 2003 SPIE and IS&T
ISSN
1017-9909
eISSN
1560-229X
DOI
10.1117/1.1525793
Publisher site
See Article on Publisher Site

Abstract

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The idea is to transform the data into the wavelet basis, where the “large” coefficients are mainly the signal, and the “smaller” ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. We evaluate several 2-D denoising procedures using test images corrupted with additive Gaussian noise. We consider global, level-dependent, and subband-dependent implementations of these techniques. Our results, using the mean squared error as a measure of the quality of denoising, show that the SureShrink and the BayesShrink methods consistently outperform the other wavelet-based techniques. In contrast, we found that a combination of simple spatial filters lead to images that were grainier with smoother edges, though the error was smaller than in the wavelet-based methods. © 2003 SPIE and IS&T.

Journal

Journal of Electronic Imaging – SPIE

Published: Jan 1, 2003

There are no references for this article.